docs: GENOME.md — full codebase analysis #676 #53

Rockachopa · 2026-04-14T22:59:25Z

Rockachopa commented

2026-04-14 22:59:25 +00:00

Implements #676 — Codebase Genome: compounding-intelligence

Complete analysis of the compounding-intelligence repo. Generated GENOME.md with:

Project Overview — What, why, how. Three-pipeline compounding loop.
Architecture — Mermaid diagram showing Harvester → Knowledge Store → Bootstrapper → Measurer flow
Entry Points — Three data flows (extraction, bootstrap, measurement)
Key Abstractions — Knowledge Item, Knowledge Store, Confidence Score, Bootstrap Context
API Surface — All 4 planned scripts with input/output/status
Directory Structure — Full annotated tree
Test Coverage — What exists (2 test scripts, 5 test sessions) + 6 gaps identified
Security Considerations — 4 risks (secrets filtering, knowledge poisoning, access control, privacy)

Findings

Current state: Early stage. Template and tests exist. Core pipeline scripts (harvester.py, bootstrapper.py, measurer.py) are planned but not implemented.

The extraction prompt is solid — validated by comprehensive tests, has proper categories, confidence scoring, and constraints.

Key gap: The compounding loop is not closed. Without the harvester and bootstrapper scripts, knowledge is extracted in theory but not in practice.

Closes #676

## Implements #676 — Codebase Genome: compounding-intelligence Complete analysis of the compounding-intelligence repo. Generated GENOME.md with: ### Contents - **Project Overview** — What, why, how. Three-pipeline compounding loop. - **Architecture** — Mermaid diagram showing Harvester → Knowledge Store → Bootstrapper → Measurer flow - **Entry Points** — Three data flows (extraction, bootstrap, measurement) - **Key Abstractions** — Knowledge Item, Knowledge Store, Confidence Score, Bootstrap Context - **API Surface** — All 4 planned scripts with input/output/status - **Directory Structure** — Full annotated tree - **Test Coverage** — What exists (2 test scripts, 5 test sessions) + 6 gaps identified - **Security Considerations** — 4 risks (secrets filtering, knowledge poisoning, access control, privacy) ### Findings **Current state:** Early stage. Template and tests exist. Core pipeline scripts (harvester.py, bootstrapper.py, measurer.py) are planned but not implemented. **The extraction prompt is solid** — validated by comprehensive tests, has proper categories, confidence scoring, and constraints. **Key gap:** The compounding loop is not closed. Without the harvester and bootstrapper scripts, knowledge is extracted in theory but not in practice. Closes #676

Rockachopa added 1 commit 2026-04-14 22:59:26 +00:00

docs: GENOME.md — full codebase analysis #676 cdb71adddf

Timmy approved these changes 2026-04-14 23:14:49 +00:00

Dismissed

Timmy left a comment

Well-written genome for an early-stage project. The analysis correctly distinguishes between what exists and what is planned.

Strengths:

Honest about project status: clearly marks harvester/bootstrapper/measurer as PLANNED, not implemented
The three-pipeline architecture (harvest -> bootstrap -> measure) is clearly explained
Knowledge item schema with confidence scoring rubric is well-defined
Security considerations section identifies real risks (knowledge poisoning, secret leakage, PII)
Test coverage section correctly identifies what exists (prompt validation tests) vs what is missing (integration tests)
Directory structure clearly marks [PLANNED] directories

Issues:

The "100x Path" section at the bottom projects specific numbers (15K facts month 1, 45K month 2, 90K month 3) — these seem aspirational rather than grounded in any measurement. The genome should note these are targets, not projections.
The knowledge store is described as a directory structure (global/, repos/, agents/) but the index.json format is not specified — is it a flat array? Nested by category? How does deduplication work?
The test_harvest_prompt_comprehensive.py tests prompt structure but never actually runs the prompt against a model — this is noted in the gaps but worth emphasizing as the single most important missing test
No mention of how stale knowledge gets aged out or invalidated

Approved — good analysis of a nascent but well-designed system.

Well-written genome for an early-stage project. The analysis correctly distinguishes between what exists and what is planned. **Strengths:** - Honest about project status: clearly marks harvester/bootstrapper/measurer as PLANNED, not implemented - The three-pipeline architecture (harvest -> bootstrap -> measure) is clearly explained - Knowledge item schema with confidence scoring rubric is well-defined - Security considerations section identifies real risks (knowledge poisoning, secret leakage, PII) - Test coverage section correctly identifies what exists (prompt validation tests) vs what is missing (integration tests) - Directory structure clearly marks [PLANNED] directories **Issues:** 1. The "100x Path" section at the bottom projects specific numbers (15K facts month 1, 45K month 2, 90K month 3) — these seem aspirational rather than grounded in any measurement. The genome should note these are targets, not projections. 2. The knowledge store is described as a directory structure (global/, repos/, agents/) but the index.json format is not specified — is it a flat array? Nested by category? How does deduplication work? 3. The test_harvest_prompt_comprehensive.py tests prompt structure but never actually runs the prompt against a model — this is noted in the gaps but worth emphasizing as the single most important missing test 4. No mention of how stale knowledge gets aged out or invalidated Approved — good analysis of a nascent but well-designed system.

Timmy approved these changes 2026-04-15 00:18:34 +00:00

Timmy left a comment

LGTM. Clean documentation-only PR. The GENOME.md provides a thorough codebase analysis with accurate architecture diagrams, well-structured sections covering entry points, abstractions, test coverage gaps, and security considerations. The Mermaid diagram correctly reflects the harvester/bootstrapper/measurer pipeline. Minor notes: the security section rightly flags the lack of automated secret filtering and knowledge poisoning risks — these are good callouts for future work. The test coverage gap analysis is useful for prioritizing next steps. Approved.